---
pretty_name: "Plan B — Contrarian Deep-Value US Equities & AI 10-K Analysis"
license: cc-by-nc-4.0
language:
  - en
size_categories:
  - n<1K
task_categories:
  - tabular-classification
  - tabular-regression
  - text-generation
  - time-series-forecasting
tags:
  - finance
  - equities
  - stocks
  - sec-edgar
  - xbrl
  - value-investing
  - fundamental-analysis
  - 10-k
  - form-4
  - insider-trading
  - financial-nlp
configs:
  - config_name: daily_rankings
    default: true
    data_files:
      - split: train
        path: daily_rankings.jsonl
  - config_name: ai_10k_memos
    data_files:
      - split: train
        path: ai_10k_memos.jsonl
  - config_name: insider_trades
    data_files:
      - split: train
        path: insider_trades.jsonl
  - config_name: scan_stats
    data_files:
      - split: train
        path: scan_stats.jsonl
---

# Plan B — Contrarian Deep-Value US Equities & AI 10-K Analysis

**Plan B** is a daily quantitative and fundamental research dataset covering deeply discounted, financially resilient, and heavily beaten-down US operating companies across the **NYSE** and **Nasdaq**.

Every trading day, Plan B screens the entire US equity universe (~6,000 SEC XBRL filers) using primary **SEC EDGAR XBRL frames**, **3-year weekly price series**, **SEC Form 4 open-market insider transactions**, **news sentiment**, and **LLM-synthesized 10-K/20-F deep-value investment memos**.

---

## Dataset Snapshot

| Metric | Value |
| :--- | :--- |
| **Coverage Window** | `2026-11-02` → `2026-11-06` (5 daily scans) |
| **Unique Shortlisted Companies** | 54 NYSE/Nasdaq tickers |
| **Daily Ranking Observations** | 200 rows (68 features per row) |
| **Structured AI 10-K/20-F Memos** | 16 filings analyzed |
| **Open-Market Form 4 Insider Trades** | 305 verified transactions |
| **Update Frequency** | Daily (automated post-market scan) |
| **License** | `CC-BY-NC-4.0` (Research & Non-Commercial) |

---

## Dataset Structure & Files

Each table is provided in both **CSV** (ideal for Kaggle, Pandas, R, and DuckDB) and **JSON Lines (`.jsonl`)** (ideal for Hugging Face `datasets`, Spark, and LLM pipelines).

| Table / Config | Files | Primary Key | Description |
| :--- | :--- | :--- | :--- |
| **`daily_rankings`** *(default)* | `daily_rankings.csv`<br>`daily_rankings.jsonl` | `(date, ticker)` | Daily ranked contrarian shortlist (~40 companies/day) with composite scores, valuation ratios, SEC XBRL balance-sheet & cash-flow fundamentals, insider totals, and AI 10-K verdict summary. |
| **`ai_10k_memos`** | `ai_10k_memos.csv`<br>`ai_10k_memos.jsonl` | `(ticker, accession)` | Skeptical value-investor memos generated from Item 1 (Business), Item 1A (Risk Factors), and Item 7 (MD&A) of each company's latest SEC Form 10-K or 20-F. |
| **`insider_trades`** | `insider_trades.csv`<br>`insider_trades.jsonl` | `(ticker, owner, kind, date, shares, form4_url)` | Open-market insider purchases (`P`) and sales (`S`) parsed directly from SEC Form 4 XML filings over the trailing 180 days (excluding option grants/awards). |
| **`scan_stats`** | `scan_stats.csv`<br>`scan_stats.jsonl` | `date` | Daily universe screening funnel: total filers, usable XBRL fundamentals, priced equities, and shortlisted count. |

---

## Screening & Ranking Methodology

Plan B follows a strict Graham–Klarman–Buffett deep-value discipline. Financial institutions (SIC 6000–6799: banks, insurers, REITs, SPACs) are excluded so balance-sheet ratios remain comparable across operating businesses.

### 1. Hard Quantitative Screen (`Cheap + Safe + Hated`)
A company must pass **all three** pillars simultaneously:
- **Cheap:** P/E ≤ 12, **or** EV/EBIT ≤ 8, **or** trading below Net Current Asset Value (`NCAV / Market Cap ≥ 1.0`, a classic Benjamin Graham net-net).
- **Safe:** Positive stockholders' equity, current ratio ≥ 1.2, total liabilities ≤ 2.0× equity (or net cash position), 3-year share dilution ≤ 25%, and positive operating cash flow if net income is negative.
- **Hated:** Share price down at least **40%** from its 3-year weekly high (`dd_3y ≤ -0.40`), with market cap ≥ $50M and share price ≥ $1.00.

### 2. Composite Score (`0–100`)
- **Base Weighting:** 40% **Cheap** (`cheap_score`) + 30% **Safe** (`safe_score`) + 30% **Hated** (`hated_score`, blending price drawdown and headline negativity `news_neg`).
- **Bonuses:** Net open-market Form 4 insider buying (up to +12 pts) and 3-year share count reduction via buybacks (+4 pts).
- **Penalties:** Share dilution, negative free cash flow, accrual divergence (net income significantly exceeding operating cash flow or operating income), one-off profits following consecutive loss years, and high goodwill/M&A intensity.
- **AI 10-K Adjustment:** After reading the 10-K filing, the composite score is adjusted by the AI analyst's verdict (`CONTRARIAN BUY CANDIDATE`, `WATCH`, or `AVOID`) and conviction (`0–100`).

---

## Quickstart Usage

### Python (`datasets` — Hugging Face)
```python
from datasets import load_dataset

# Load default config (daily_rankings)
rankings = load_dataset("elsiddik/planb-deep-value", "daily_rankings", split="train")
print(rankings.to_pandas()[["date", "rank", "ticker", "score", "pe", "ev_ebit", "dd_3y", "ai_verdict"]].head())

# Load structured 10-K AI memos
memos = load_dataset("elsiddik/planb-deep-value", "ai_10k_memos", split="train")

# Load Form 4 open-market insider trades
insiders = load_dataset("elsiddik/planb-deep-value", "insider_trades", split="train")
```

### Python (`pandas` — Kaggle / Local CSV)
```python
import pandas as pd

df = pd.read_csv("daily_rankings.csv")
memos = pd.read_csv("ai_10k_memos.csv")
insiders = pd.read_csv("insider_trades.csv")

# Filter highest-conviction ideas with net cash and insider buying
top_ideas = df[(df["net_cash"] > 0) & (df["insider_buy_value"] > 0)].sort_values(["date", "rank"])
print(top_ideas[["date", "ticker", "name", "score", "pe", "ev_ebit", "net_cash_pct", "ai_verdict"]].head(10))
```

---

## Data Dictionary

### 1. `daily_rankings` (68 Columns)

| Column | Type | Description |
| :--- | :--- | :--- |
| `date` | `string` | Scan date (`YYYY-MM-DD`). |
| `rank` | `int` | Daily rank (`1` = highest composite score). |
| `ticker`, `name`, `cik`, `exchange`, `sector` | `string`/`int` | Ticker symbol, SEC registrant name, SEC CIK identifier, exchange (`NYSE`/`Nasdaq`), and SIC industry description. |
| `score` | `float` | Final Plan B composite score (`0–100`). |
| `cheap_score`, `safe_score`, `hated_price_score`, `hated_score` | `float` | Normalized sub-scores (`0.0–1.0`) for valuation, balance-sheet safety, price drawdown, and combined hated score. |
| `news_neg`, `hate_why` | `float`/`string` | Headline negativity score (`0.0` loved → `1.0` hated) and short explanation of market pessimism. |
| `first_seen`, `return_since_first` | `string`/`float` | Date the ticker first entered the shortlist and cumulative price return since `first_seen`. |
| `bonus`, `flags` | `list` / `string` | Score bonuses (e.g., insider buying, shrinking shares) and accounting/risk flags. |
| `price`, `mcap`, `ev` | `float` | Latest share price (USD), market capitalization (USD), and enterprise value (`Market Cap + Debt - Cash`, USD). |
| `dd_3y`, `ret_1y`, `high_3y`, `high_52w`, `low_52w`, `off_low` | `float` | Drawdown from 3-year high (`-0.75` = down 75%), 1-year return, 3-year/52-week highs & lows, and percentage above 52-week low. |
| `pe`, `ev_ebit`, `pb` | `float` | Price-to-Earnings, Enterprise Value-to-EBIT (capped at `0` when EV is negative), and Price-to-Book ratio. |
| `earnings_yield`, `ebit_ev`, `fcf_yield`, `book_yield` | `float` | Inverse valuation yields: `NI / MCap`, `EBIT / EV`, `FCF / MCap`, and `Equity / MCap`. |
| `ncav`, `netnet`, `net_cash`, `net_cash_pct` | `float` | Net Current Asset Value (`CA - Total Liabilities`), `NCAV / MCap`, Net Cash (`Cash - Debt`), and `Net Cash / MCap`. |
| `current_ratio`, `liab_eq`, `fcf`, `profit_years` | `float`/`int` | Current assets / current liabilities, total liabilities / equity, free cash flow (`CFO - CapEx`), and profitable years out of the last 3 FYs (`0–3`). |
| `share_chg_3y`, `rev_growth`, `buyback_yield`, `div_yield`, `goodwill_pct`, `acq_pct` | `float` | 3-year share count change, YoY revenue growth, buybacks/MCap, dividends/MCap, goodwill/assets, and acquisitions/assets. |
| `period`, `fy`, `assets`, `ca`, `cl`, `cash`, `equity`, `goodwill`, `debt`, `debt_known`, `liab` | `mixed` | Raw SEC XBRL balance-sheet values (USD) and reporting period (`CY2026Q3I`, etc.). |
| `shares`, `shares_old`, `shares_old_period` | `float`/`string` | Latest common shares outstanding and shares outstanding 3 years prior. |
| `ni`, `ni_1`, `ni_2`, `oi`, `cfo`, `capex`, `buyback`, `dividends`, `acquisitions`, `rev`, `rev_1` | `float` | Raw SEC XBRL annual income statement and cash-flow values (USD) for the latest and prior fiscal years. |
| `insider_buy_value`, `insider_sell_value`, `insider_buyers_count` | `float`/`int` | Trailing 180-day open-market Form 4 purchase USD total, sale USD total, and distinct buyer count. |
| `tenk_date`, `tenk_accession`, `tenk_form`, `tenk_url` | `string` | Filing date, SEC accession number, form type (`10-K` / `20-F`), and direct SEC EDGAR archive URL. |
| `ai_verdict`, `ai_conviction`, `ai_balance_sheet`, `ai_value_trap_risk`, `ai_one_liner` | `mixed` | AI 10-K analyst summary fields (`CONTRARIAN BUY CANDIDATE` / `WATCH` / `AVOID`, conviction `0–100`, balance sheet rating, value-trap risk, and one-line thesis). |

### 2. `ai_10k_memos` (21 Columns)

| Column | Type | Description |
| :--- | :--- | :--- |
| `ticker`, `name`, `cik`, `exchange`, `sector` | `string`/`int` | Company identifiers and SIC industry classification. |
| `accession`, `filing_date`, `read_on`, `filing_url` | `string` | SEC accession number, filing date, scan date analyzed, and direct EDGAR filing URL. |
| `verdict`, `conviction`, `balance_sheet`, `value_trap_risk` | `string`/`int` | Overall verdict, conviction (`0–100`), balance-sheet rating (`fortress`/`solid`/`stretched`/`weak`), and value-trap risk (`low`/`medium`/`high`). |
| `one_liner`, `business`, `why_hated`, `capital_allocation`, `management` | `string` | Concise investment synthesis covering the business model, market pessimism, buybacks/dividends/M&A, and leadership. |
| `bull`, `bear`, `red_flags` | `list` / `string` | Bullet lists of key bull points, bear points, and accounting/governance/footnote red flags from the 10-K. |

### 3. `insider_trades` (11 Columns)

| Column | Type | Description |
| :--- | :--- | :--- |
| `date` | `string` | Transaction date (`YYYY-MM-DD`). |
| `ticker`, `company_name`, `cik` | `string`/`int` | Issuer ticker, company name, and SEC CIK. |
| `owner`, `title` | `string` | Reporting insider name and relationship/officer title (`CEO`, `CFO`, `Director`, `10% owner`). |
| `kind` | `string` | Open-market transaction code: `P` (Purchase) or `S` (Sale). |
| `shares`, `price`, `value` | `float` | Number of shares traded, execution price per share (USD), and total dollar value (USD). |
| `form4_url` | `string` | Direct URL to the primary SEC Form 4 XML filing on EDGAR. |

---

## Data Integrity, Privacy & Caveats

- **Zero Credentials / Zero PII Guarantee:** Every export passes an automated security gate (`assertNoCredentials`) that enforces an explicit column allowlist, scrubs email addresses, and scans against secret/token patterns before writing or uploading.
- **XBRL Tagging Nuances:** SEC fundamentals reflect company-reported US-GAAP XBRL tags. Unusual tagging or dual-class share structures can occasionally distort market cap or debt ratios; always cross-check the linked SEC filing (`tenk_url`).
- **Research Only:** This dataset is for quantitative, academic, and machine-learning research only and never constitutes financial or investment advice.